gemma-4-12B-it Offline on PC No-Internet Version Local Guide

For the fastest local setup of this model, enabling Windows Features is best.

Review and follow the instructions below.

The setup auto-downloads all needed files (several GBs).

Your resources are automatically evaluated to lock in the premium configuration.

🧮 Hash-code: 910af9a9959b3820c88b35077c4c9358 • 📆 2026-06-25
  • CPU: modern architecture (Zen 3 / Alder Lake minimum)
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk: 150+ GB for high-context vector database storage
  • Graphics: TensorRT-LLM / vLLM inference engine compatible chip

The Gemma-4-12B-it model delivers state‑of‑the‑art performance across a wide range of language tasks. Its 12‑billion parameter architecture enables fast inference while maintaining high accuracy on reasoning benchmarks. The model supports a 2048‑token context window, allowing it to understand longer passages and generate coherent responses. Trained on diverse web‑scale datasets, it exhibits strong multilingual capabilities and a nuanced understanding of technical terminology. Compared to its predecessors, Gemma‑4‑12B‑it shows a 15% improvement in reading comprehension and a 10% boost in code generation tasks. The following table summarizes its key specifications:

Parameter Count 12 billion
Context Length 2048 tokens
Training Data Web‑scale multilingual corpus
Reading Comprehension 85% accuracy
Code Generation 78% pass@1
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